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          <h1 class="entry-title ">Download</h1><hr class="featurette-divider __before_content">        </header>
        
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          <p>Below, you can find all currently available downloads. A README and various scripts for inspection, preparation, and evaluation can be found in our <a href="https://github.com/mcordts/cityscapesScripts" target="_blank">git repository</a>.</p>
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    <div id='csPackageCategory'><h4>The following packages are available for download</h4><div id='csPackageList'><ul ><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/jena00_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=1'>gtFine_trainvaltest.zip (241MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=1' target='_blank'>[md5]</a></em> fine annotations for train and val sets (3475 annotated images) and dummy annotations (ignore regions) for the test set (1525 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/erlangen00_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=2'>gtCoarse.zip (1.3GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=2' target='_blank'>[md5]</a></em> coarse annotations for train and val set (3475 annotated images) and train_extra (19998 annotated images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=3'>leftImg8bit_trainvaltest.zip (11GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=3' target='_blank'>[md5]</a></em> left 8-bit images - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=4'>leftImg8bit_trainextra.zip (44GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=4' target='_blank'>[md5]</a></em> left 8-bit images - trainextra set (19998 images, note that the image "troisdorf_000000_000073_leftImg8bit.png" is corrupt/black)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/cameraDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=8'>camera_trainvaltest.zip (2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=8' target='_blank'>[md5]</a></em> intrinsic and extrinsic camera parameters for train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/cameraDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=9'>camera_trainextra.zip (8MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=9' target='_blank'>[md5]</a></em> intrinsic and extrinsic camera parameters for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=10'>vehicle_trainvaltest.zip (2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=10' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=11'>vehicle_trainextra.zip (7MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=11' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/demoVideoDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=12'>leftImg8bit_demoVideo.zip (6.6GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=12' target='_blank'>[md5]</a></em> video sequences for qualitative evaluation, left 8-bit images only</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/01/downloadblurred.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=32'>leftImg8bit_blurred.zip (11GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=32' target='_blank'>[md5]</a></em> Left 8-bit images with blurred faces and license plates. Please compute results on the original images but use the blurred ones for visualization. We thank <a href="https://www.mapillary.com" target="_blank">Mapillary</a> for blurring the images.</p></li></ul></div></div>
    <div id='csPackageCategory'><hr><h4>The following packages from third party publications are available for download</h4><p>If you use the packages in your research, please cite both publications, <a href='https://www.cityscapes-dataset.com/citation/'>Cityscapes</a> and the work listed in conjunction with the package. Please contact the respective authors directly in case of questions.</p><div id='csPackageList'><ul ><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/08/citypersonsDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=28'>gtBbox_cityPersons_trainval.zip (2.2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=28' target='_blank'>[md5]</a></em> CityPersons bounding box annotations of people for train and val sets (3475 annotated images). Citation: S. Zhang, R. Benenson, and B. Schiele, <a href="http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_CityPersons_A_Diverse_CVPR_2017_paper.html" target="_blank">"CityPersons: A Diverse Dataset for Pedestrian Detection"</a>, CVPR 2017.</p></li></ul></div></div>
    <div id='csPackageCategory'><hr><h4>The following packages are available upon request. If you require any of them for your research, please send an <a href='mailto:mail@cityscapes-dataset.net?subject=Cityscapes%20Package%20Request'>email</a> including your Cityscapes username and a list of required packages.</h4><div id='csPackageList'><ul><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg8bit_trainvaltest.zip</em> right 8-bit images - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg8bit_trainextra.zip</em> right 8-bit images - trainextra set (19998 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>disparity_trainvaltest.zip</em> precomputed depth maps using SGM for train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>disparity_trainextra.zip</em> precomputed depth maps using SGM for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/demoVideoDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>all_demoVideo.zip</em> video sequences for qualitative evaluation, all types of data</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_sequence_trainvaltest.zip</em> 30-frame snippets (17Hz) surrounding each left 8-bit image (-19 | +10) from the train, val, and test sets (150000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/timestampDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>timestamp_sequence.zip</em> timestamps for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>vehicle_sequence.zip</em> vehicle odometry + GPS coordinates + temperature for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg8bit_sequence_trainvaltest.zip</em> 30-frame snippets (17Hz) surrounding each right 8-bit image (-19 | +10) from the train, val, and test sets (150000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>disparity_sequence_trainvaltest.zip</em> precomputed depth maps using SGM for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_allFrames_frankfurt.zip</em> left 8-bit images for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/timestampDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>timestamp_allFrames_frankfurt.zip</em> timestamps for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>vehicle_allFrames_frankfurt.zip</em> vehicle odometry + GPS coordinates + temperature for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrLeftDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg16bit_trainvaltest.zip</em> left HDR images stored as 16-bit PNGs - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg8bit_allFrames_frankfurt.zip</em> right 8-bit images for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrLeftDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg16bit_trainextra.zip</em> left HDR images stored as 16-bit PNGs - trainextra set (19998 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrRightDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg16bit_trainvaltest.zip</em> right HDR images stored as 16-bit PNGs - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrRightDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>rightImg16bit_trainextra.zip</em> right HDR images stored as 16-bit PNGs - trainextra set (19998 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/01/foggydbfdownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_trainval_foggyDBF.zip</em> Foggy DBF images (left 8-bit) for train and val sets (3475 images). Citation: C. Sakaridis, D. Dai, S. Hecker, and L. Van Gool, <a href="https://www.vision.ee.ethz.ch/~csakarid/Model_adaptation_SFSU_dense/">"Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding"</a>, ECCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/12/foggyDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_trainvaltest_foggy.zip</em> Foggy images (left 8-bit) for train, val, and test sets (5000 images). Citation: C. Sakaridis, D. Dai, and L. Van Gool, <a href="http://people.ee.ethz.ch/~csakarid/SFSU_synthetic/" target="_blank">"Semantic Foggy Scene Understanding with Synthetic Data"</a>, IJCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/12/foggyDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_trainextra_foggy.zip</em> Foggy images (left 8-bit) for trainextra set (19997 images). Citation: C. Sakaridis, D. Dai, and L. Van Gool, <a href="http://people.ee.ethz.ch/~csakarid/SFSU_synthetic/" target="_blank">"Semantic Foggy Scene Understanding with Synthetic Data"</a>, IJCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/07/raindownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px;'><p><em>leftImg8bit_trainval_rain.zip</em> Rain images (left 8-bit) for parts of train and val sets (295 images, 36 variants each). Citation: X. Hu, C.-W. Fu, L. Zhu, and P.-A. Heng, <a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Hu_Depth-Attentional_Features_for_Single-Image_Rain_Removal_CVPR_2019_paper.html" target="_blank">"Depth-Attentional Features for Single-Image Rain Removal"</a>, CVPR 2019.</p></li></ul></div></div>
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